Spatial and temporal analyses of winter falls in urban areas

Engineering notes

Modelling showed that the number of falls was significantly influenced by temperature differences ( p = 0.011), wind gusts ( p = 0.015), night-time precipitation ( p = 0.005), number of bus routes ( p < 0.001), number of buildings ( p = 0.095), proportion of low-rise buildings ( p = 0.007) and proportion of special areas, including industrial areas ( p = 0.047).

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Falls are the second leading cause of unintentional injury deaths worldwide. These injuries lead to temporary or permanent disability of the population, have an impact on health care and societal costs. The urgency of solving this problem is increased by calls to promote walking for environmental reasons and to reduce physical inactivity, as well as by the ageing population, which is characterised by the highest number of fatal falls. The aim of the study is to provide a better understanding of the factors related to weather and location that influence the distribution of the falls. The analysis utilises daily records of winter outdoor falls across 82 microdistricts within the city of Perm, Russia, spanning the years 2015 through 2019. Special emphasis is placed on detecting patterns of fall concentrations in diverse urban areas, incorporating data from four meteorological stations encompassing 28 distinct weather parameters. The analysis was carried out using spatial analysis methods: Global Moran’s Index, spatial lag panel model with additional endogenous variables. Results: Moran’s Index calculations showed that falls exhibited spatial autocorrelation. Modelling showed that the number of falls was significantly influenced by temperature differences ( p = 0.011), wind gusts ( p = 0.015), night-time precipitation ( p = 0.005), number of bus routes ( p < 0.001), number of buildings ( p = 0.095), proportion of low-rise buildings ( p = 0.007) and proportion of special areas, including industrial areas ( p = 0.047). Analysis of locational factors distils the most vulnerable communities across the city and highlights the need for different injury prevention policy profiles for each neighbourhood. The study demonstrated the importance of combining weather parameters and location factors in predicting outdoor falls. These findings should be considered when designing policies for ensuring pedestrian safety and developing action plans to prevent falls.

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